Lacework Inc.

Sprint Communications Comapny L.p.

Netlogic Microsystems, Inc

Fortinet, Inc.

Broadcom Corporation

Murat Bog

Yijou Chen

Vikram Kapoor

Harish Kumar Bharat Singh

Samuel Joseph Pullara, Iii

Harold Wayne Johnson

Paolo L Narvaez

Sanjay Kalra

William Lee Edwards

Bryan Lee Gorman

Frank Anthony DeNap

Martin Joseph Kaplan

John Arndt Strand, Iii

Michael Thomas Swink

Joseph Moore

Matthew Barrow

Timothy Gene Kelley

Shannon P Silvus

Chaoxin Charles Qiu

Benjamin J Ewy

Activities within a network environment are monitored (e.g., using agents). At least a portion of the monitored activities are used to generate a logical graph model. The generated logical graph model is used to determine an anomaly. The detected anomaly is recorded and can be used to generate an alert.

Detecting anomalies in a network environment

An illustrative method includes generating, based on log data associated with at least one user session in a network environment associated with a user, a logical graph, wherein the logical graph comprises: (1) a first node corresponding to the user, (2) a plurality of additional nodes, and (3) a set of edges connecting the first node to one or more of the additional nodes, wherein each edge in the set of edges represents a change in behavior of the user; using the logical graph to detect an anomaly, wherein detecting the anomaly includes determining that a change has been made to at least one edge included in the set of edges; and generating, in response to detecting the anomaly, an alert.

Detecting threats against computing resources based on user behavior changes

An illustrative method includes generating a logical graph by performing a clustering operation with respect to log data associated with one or more machines, the clustering operation performed using a first clustering criteria and causing the logical graph to initially include a first set of nodes generated in accordance with relationship requirements of an underlying model and a first set of edges representing communication between nodes included in the first set of nodes; and reclustering, using a second clustering criteria, the logical graph to include, in place of the first set of nodes, a second set of nodes generated while maintaining the relationship requirements of the underlying model and a second set of edges representing communication between nodes included in the second set of nodes.

Graph reclustering based on different clustering criteria

An illustrative method includes determining that a first user login session and a second user login session have a parent-child relationship that indicates that a particular user is associated with both the first and second user login sessions and linking first user login activity performed during the first user login session and second user login activity performed during the second user login session to the user.

Tracking of user login sessions

A logical graph is generated using at least a portion of log data received from a set of agents executing on one or more nodes in one or more data centers. The logical graph is augmented using data obtained from one or more agents executing in containerized environments, including by representing communications between pods within the logical graph. The augmented logical graph is used to detect an anomaly.

Alerting based on pod communication-based logical graph

Log data associated with at least one user session in a network environment associated with an original user is received. A logical graph is generated using at least a portion of the received log data. One example of such a logical graph is a privilege change graph that models privilege changes between processes. Another example of such a logical graph is a user login graph that models machines with which the original user interacts. Another example of such a logical graph is a machine-server graph that clusters machines into nodes based on resources executing on the machine. The generated logical graph is used to detect an anomaly.

User session-based generation of logical graphs and detection of anomalies

Using graph-based models to identify anomalies in a network environment

A logical graph is generated using at least a portion of log data received from a set of agents executing on one or more nodes in one or more data centers. The logical graph is generated at least in part by clustering a first set of nodes using a first clustering criteria. The logical graph is augmented at least in part by performing a reclustering operation using a second clustering criteria.

Hierarchical graph analysis

Pod communication graph

Using graph-based models to identify datacenter anomalies

First information associated with a first user login activity is received, as is second information associated with a second user login activity. A determination is made, using the received first and second information, that the first user login activity and second user login activity have a parent-child relationship. The first user login activity and the second user login activity are linked to at least one user and a process.

Extended user session tracking

Graph-based user tracking and threat detection

Network activity data is received, for example, from a set of agents reporting collectively information about a set of hosts. The received network activity data is used to identify a user login activity. A logical graph that links the user login activity to at least one user and at least one process is generated.

Log data associated with at least one user session associated with an original user is received. A logical graph is generated using at least a portion of the received log data. One example of such a logical graph is a privilege change graph that models privilege changes between processes. Another example of such a logical graph is a user login graph that models machines with which the original user interacts. Another example of such a logical graph is a machine-server graph that clusters machines into nodes based on resources executing on the machine. The generated logical graph is used to detect an anomaly. The detected anomaly is recorded.

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Fremont, CA, United States of America

Murat Bog